Training Method of Road Condition Prediction Model, Road Condition Prediction Method and Device

Abstract

The present application discloses a training method, a road condition prediction method and a device for a road condition prediction model. By performing data transformation on a historical road condition training set, a plurality of target graph data are obtained, and a preset residual graph convolutional model is used to extract features from the plurality of target graph data to obtain a plurality of residual graphs, so as to capture data features with directional correlation by using the spatial dependence relationship of the graph data, in order to combine the time correlation features of the historical data in a plurality of time periods; the plurality of residual graphs are fused and fully connected to output a visual road condition prediction value, and according to the visual road condition prediction value, a loss function of the residual graph convolutional model is calculated, and the model parameters of the residual graph convolutional model are updated based on the loss function until the residual graph convolutional model reaches a preset convergence condition, thereby obtaining a road condition prediction model, so as to make full use of global features such as time and space, construct a prediction model with visual road condition features, enrich the model prediction results and improve the model prediction accuracy.